참고문헌
- Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., and Ghemawat, S. (2016). "Tensorflow: Large-scale machine learning on heterogeneous distributed systems."arXiv preprint arXiv:1603.04467.
- Attoh-Okine, N. O. (1999). "Analysis of learning rate and momentum term in backpropagation neural network algorithm trained to predict pavement performance."Advances in Engineering Software, Vol.30, No.4, pp.291-302. https://doi.org/10.1016/S0965-9978(98)00071-4
- Baek, J., Lim, J., Kwon, S., and Kwon, B. (2015). "Performance Evaluation of Long-Life Asphalt Concrete Overlays Based on Field Survey Monitoring in National Highways."Intl. Journal of the Highway Engineers, Vol. 17, No. 3, pp.69-76.
- Bowden, G.J., Dandy, G.C., Maier, H.R. (2005). " Input determination for neural network models in water resources applications. Part 1-background and methodology."Journal of Hydrology, Vol.301, No.1-4, pp.75-92. https://doi.org/10.1016/j.jhydrol.2004.06.021
- Clevert, D. A., Unterthiner, T., Hochreiter, S. (2015). "Fast and accurate deep network learning by exponential linear units (elus)."arXiv preprint arXiv:1511.07289.
- Dahl, G. E., Sainath, T. N., Hinton, G. E. (2013). "Improving deep neural networks for LVCSR using rectified linear units and dropout."IEEE International Confedence Acoustics on Speech and Signal Processing(ICASSP), pp.8609-8613.
- Do, M. and Kwon, S. (2010). "Selection of Probability Distribution of Pavement Life Based on Reliability Method."Intl. Journal of the Highway Engineers, Vol.12, No.1. pp.61-69.
- Gopalakrishnan, K., Khaitan, S. K., Choudhary, A., Agrawal, A. (2017). "Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection."Construction and Building Materials, Vol.157, pp.322-330. https://doi.org/10.1016/j.conbuildmat.2017.09.110
- Han, D., Yoo, I., Lee, S. (2017a). "Improvement of Multivariable, Nonlinear, and Overdispersion Modeling with Deep Learning: A Case Study on Prediction of Vehicle Fuel Consumption Rate."Intl. Journal of the Highway Engineers, Vol.19, No.4, pp.1-7.
- Han, D., Do, M., Kim, B. (2017b). "Internal Property and Stochastic Deterioration Modeling of Total Pavement Condition Index for Transportation Asset Management."Intl. Journal of the Highway Engineers, Vol.19, No.5, pp.1-11.
- Han, D. (2013). "Stochastic Disaggregation and Aggregation of Localized Uncertainty in Pavement Deterioration Process." Journal Of The Korean Society Of Civil Engineers, Vol.33, No.4. pp.1651-1664. https://doi.org/10.12652/Ksce.2013.33.4.1651
- Han, D. and Do, M. (2012). "Estimation of Life Expectancy and Budget Demands based on Maintenance Strategy."Journal Of The Korean Society Of Civil Engineers, Vol.32, No.4D, pp.345-356. https://doi.org/10.12652/Ksce.2012.32.4D.345
- Hinton, G. E., Osindero, S., Tech, Y. W. (2006). "A fast learning algorithm for deep belief nets."Neural computation, Vol.18, No.7, pp.1527-1554. https://doi.org/10.1162/neco.2006.18.7.1527
- Kim, H., and Lim, H. (2016). "A Basic Study on the Prediction of Collapse of Tunnels Using Artificial Neural Network."Journal of The Korean Geotechnical Society, Vol.32, No.2, pp.5-17 https://doi.org/10.7843/KGS.2016.32.2.5
- Kobayashi, K., Kaito, K., Nam, L. (2012). "A statistical deterioration forecasting method using hidden Markov model with measurement error."Transportation Research-Part B, Vol.46, pp.544-561. https://doi.org/10.1016/j.trb.2011.11.008
- Kobayashi, K., Do, M., Han, D. (2010). "Estimation of Markovian transition probabilities for pavement deterioration forecasting." KSCE J. of Civil Engineering, Vol.14, No.3, pp.341-351.
- Ministry of Land, Infrastructure, and Transport (MOLIT) (2016). Development of Road Asset Management System Focus on Road Pavement.
- May, R., Dandy, G., and Maier, H. (2011). "Review of Input Variable Selection Methods for Artificial Neural Networks." Artificial Neural Networks-Methodological Advances and Biomedical Applications, Edited by Suzuki, K., InTech, India, pp.19-44.
- May, R.J., Maier, H.R., Dandy, G.C., Fernando, T.M.K.G. (2008). "Non-linear variable selection for artificial neural networks using partial mutual information."Environmental Modelling & Software, Vol.23, No.10-11, pp. 1312-1326. https://doi.org/10.1016/j.envsoft.2008.03.007
- Mishalani, R.G., and Madanat, S.M. (2002). "Computation of infrastructure transition probabilities using stochastic duration models."J. of Infrastructure Systems, Vol.8, No.4, pp.139-148. https://doi.org/10.1061/(ASCE)1076-0342(2002)8:4(139)
- Minsky M.L., Papert, S. A. (1969). "Perceptrons."Cambridge, MA: MIT Press.
- Nair, V., Hinton, G. E. (2010). "Rectified linear units improve restricted boltzmann machines."In Proceedings of the 27th International on Machine Learning (ICML-10), pp.809-814.
- Srivastava N., Hinton, G. E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. (2014)."Dropout: A simple way to prevent neural networks from overfitting."The Journal of Machine Learning Research, Vol.15, No.1, pp.1929-1958.
- Tan, P.N., Steinbach, M., Kumar, V. (2006). "Introduction to Data Mining", Pearson Education, Addison Wesely.
- Terzi, S. (2007). "Modeling the pavement serviceability ratio of flexible highway pavements by artificial neural networks." Construction and Building Materials, Vol.21, No.3, pp.590-593. https://doi.org/10.1016/j.conbuildmat.2005.11.001
- Werbos, P.J. (1974). "Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences."PhD thesis, Harvard University.